Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis.

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach.

Toxic encephalopathy, vision loss, and memory disorder caused by acute acrylamide exposure.

Acrylamide (ACR) is an irritant that can cause damage to the eyes, skin, and nervous and reproductive systems. This study aims to illustrate a case of central nervous system and optic nerve damage from exposure to ACR. In this case, a 49-year-old male material handler was accidentally splashed with ACR solution on both of his upper limbs. Consequently, he was admitted to the hospital with toxic encephalopathy, characterized by cerebellar ataxia and slurred speech. Magnetic resonance imaging scan, a brain computed tomography scan blood sample analyses, optic coherence tomography, electroneuromyogram, and visual evoked potentials examination were performed. After 20 days of receiving symptomatic support treatment, the patient continued to experience disturbances in consciousness. Then, he developed vision loss, memory disorders, and symptoms of peripheral neuropathy such as skin peeling, extremity weakness, and absent tendon reflexes. This case report underscores the severe consequences of acute dermal exposure to high concentrations of ACR, resulting in toxic encephalopathy, visual impairment, and memory disorders, which will contribute to a broader understanding of ACR toxicity.

An enhanced K-SVD denoising algorithm based on adaptive soft-threshold shrinkage for fault detection of wind turbine rolling bearing.

Due to nonstationary operating conditions of wind turbines and surrounding harsh working environments, the impulse features induced by bearing faults are always overwhelmed by heavy noise, which brings challenges to accurately detect rolling bearing faults. Sparse representation exhibits excellent performance in nonstationary signal analysis, but it is closely bound up with the degree of similarity between the atoms in a dictionary and signals. Therefore, this paper investigates an enhanced K-SVD denoising method based on adaptive soft-threshold shrinkage to achieve high-precision extraction of impulse signals, and applies it to fault detection of generator bearing of wind turbines. An adaptive sparse coding shrinkage soft-threshold denoising is first proposed to remove noise and harmonic interference in the residual term of dictionary updating, so that the updated atoms show obvious impact characteristics. Furthermore, a soft-threshold shrinkage function with adaptive threshold is designed to further suppress clutter in atoms of the learned dictionary, so as to obtain an optimized dictionary for recovering impulse signals. Two actual engineering cases are selected for analysis, and the envelope spectrum correlation kurtosis corresponding to the results obtained by the proposed method is significantly higher than that of other comparison methods, thus verifying its superiority in detecting rolling bearing faults.

Post-traumatic Stress Disorder: Focus on Neuroinflammation.

Post-traumatic stress disorder (PTSD), gaining increasing attention, is a multifaceted psychiatric disorder that occurs following a stressful or traumatic event or series of events. Recently, several studies showed a close relationship between PTSD and neuroinflammation. Neuroinflammation, a defense response of the nervous system, is associated with the activation of neuroimmune cells such as microglia and astrocytes and with changes in inflammatory markers. In this review, we first analyzed the relationship between neuroinflammation and PTSD: the effect of stress-derived activation of the hypothalamic-pituitary-adrenal (HPA) axis on the main immune cells in the brain and the effect of stimulated immune cells in the brain on the HPA axis. We then summarize the alteration of inflammatory markers in brain regions related to PTSD. Astrocytes are neural parenchymal cells that protect neurons by regulating the ionic microenvironment around neurons. Microglia are macrophages of the brain that coordinate the immunological response. Recent studies on these two cell types provided new insight into neuroinflammation in PTSD. These contribute to promoting comprehension of neuroinflammation, which plays a pivotal role in the pathogenesis of PTSD.

Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism.

As a domain adaptation method, the domain-adversarial neural network (DANN) can utilize the adversarial learning of the feature extractor and domain discriminator to extract the domain-invariant features, thus realizing fault identification of rolling bearings. In the cross-domain diagnosis of rolling bearing faults, how to obtain fault-related discriminative domain-invariant features from the noisy signals is a key to improving the diagnostic result. In response to this, this paper proposes an intelligent diagnosis model based on the DANN and attention mechanism to identify rolling bearing faults. In order to relieve the influence of noisy data on feature extraction and improve the quality of the learned features, the ensemble empirical mode decomposition (EEMD) is first adopted to denoise the raw sample data to weaken the influence of noise on feature extraction. Secondly, a feature extractor composed of three feature extraction modules in series is designed, and each feature extraction module is composed of a convolution layer, an attention mechanism module and a pooling layer. The feature extractor with attention mechanism enables the model to learn and retain key features related to the faults during training process. Meanwhile, the global average pooling layer is used to replace some fully connected layers in the fault classifier and domain discriminator to reduce model parameters and avoid model overfitting. Finally, the analysis using two sets of rolling bearing experimental about the performance of the presented method show that the proposed method has the potential to become a promising tool for the fault diagnosis of rolling bearings.

Severe visual loss caused by inhalational methanol poisoning in fireworks production: A report on 3 cases.

BACKGROUND: Workers in fireworks production are mainly at risk for explosion injury. However, there are few reports on the consequences of methanol poisoning in fireworks laborers. CASE PRESENTATION: We report on three patients with visual loss caused by inhalation exposure to high concentrations of methanol, who were engaged in the granulation process of the fireworks manufacturing industry. They presented with severe metabolic acidosis and visual impairments, accompanied by headache, chest tightness, shortness of breath, dizziness, and vomiting. All were diagnosed with acute methanol poisoning. One patient developed bilateral blindness and two patients improved after timely hemodialysis treatment. CONCLUSIONS: These case reports emphasize the risk of methanol poisoning in the fireworks industry or other factories using commercial alcohol with high methanol content. Early hemodialysis intervention and metabolic acidosis correction are crucial for rescuing visual impairment caused by methanol exposure. Awareness and supervision of commercial alcohol use are indispensable for similar industrial processes.

Measurement of CS2 Absorption Cross-Sections in the 188-215 nm Region at Room Temperature and Atmospheric Pressure.

Carbon disulfide, an important sulfur-containing species, has strong absorption lines in the wavelength range of 188 nm to 215 nm. It is difficult to accurately measure the absorption cross sections of carbon disulfide because carbon disulfide will be easily converted into carbon sulfide when it is exposed to ultraviolet light. In this study, the absorption cross sections of carbon disulfide were measured by reducing carbon disulfide conversion. The factors affecting carbon disulfide conversion, including gas flow rate, ultraviolet light intensity, and duration of illumination, were studied to reduce the conversion of carbon disulfide by controlling experimental conditions in the experiment. Finally, the absorption cross sections of carbon disulfide at room temperature and atmospheric pressure were calculated using the absorption spectrum and the carbon disulfide concentration in the absence of carbon disulfide conversion. The wavelengths of 16 absorption peaks on the carbon disulfide absorption cross sections of the vibration change were marked. Carbon disulfide has the maximum absorption cross section of 4.5 × 10-16 cm2/molecule at a wavelength of 198.10 nm.

Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach.

BACKGROUND: Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE: This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS: A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS: We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS: To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

An enhanced rolling bearing fault detection method combining sparse code shrinkage denoising with fast spectral correlation.

Rolling bearings are important supporting components widely used in rotating machinery and are prone to failure, it is thus important to perform fault detection of rolling bearing quickly and accurately. Aiming at the problem that it is difficult to extract the weak impulses buried in strong background noise in rolling bearing fault diagnosis, this paper proposes an enhanced fault detection method combining sparse code shrinkage denoising with fast spectral correlation according to the cyclic statistical properties of defective bearing vibration signals. First, in view of the non-Gaussian statistical properties of the periodic impulses caused by the localized bearing defect in vibration signals, the sparse code shrinkage algorithm is employed to denoise the original noisy signal, thereby highlighting the periodic impulses. Then, the Fast Spectral Correlation (Fast-SC) algorithm is used to process the denoised signal to get the cyclic spectral correlation. Finally, the squared enhanced envelope spectrum (SEES) is presented to effectively detect and identify the rolling bearing faults. Experimental results demonstrate the validity and superiority of the proposed method in rolling bearing fault detection through the comparison with the Fast-SC, spectral kurtosis and Infogram.

Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance.

In the gear fault diagnosis, the emergence of periodic impulse components in vibration signals is an important symptom of gear failure. However, heavy background noise makes it difficult to extract the weak periodic impulse features. Therefore, the paper presents an impact fault detection method of gearbox by combining variational mode decomposition (VMD) with coupled underdamped stochastic resonance (CUSR) to extract the periodic impulse features. First, the adaptive VMD is presented to decompose the vibration signal into several intrinsic mode functions (IMFs), which can automatically determine the appropriate mode number according to the correlation kurtosis (CK) of decomposition results and extract the sensitive IMF component containing the main fault information. Next, the adaptive CUSR method is developed to analyze the selected sensitive IMF component, and the optimal system parameters are obtained by the genetic algorithm using the CK index as optimization objective function. Finally, the periodic impulse features are extracted by the output signal of CUSR system accurately. Experiments and engineering application verify the effectiveness and superiority of the proposed adaptive VMD-CUSR method for extracting the periodic impulse features in gear fault diagnosis compared to other methods.

Self-Polarization of PVDF Film Triggered by Hydrophilic Treatment for Pyroelectric Sensor with Ultra-Low Piezoelectric Noise.

Polyvinylidene fluoride (PVDF) films possess multifunctional ability for piezo/pyro/ferroelectronic applications. One critical challenge of the traditional techniques is the complicated fabrication process for obtaining the poled films. In this work, the PVDF film is facilely prepared by the solution cast on hydrophilically treated substrates. The obtained PVDF films exhibit fairly good pyroelectricity comparable to those fabricated by thermal poling, indicating the film is self-polarized. This result is attributed to the hydrogen-bonding-induced orderly arrangement of the first sub-nanolayer at the bottom, which serves as a "seed layer" and triggered alignment of the rest of the film in a layer-by-layer approach. Additionally, to suppress the piezoelectric noise, a pyroelectric sensor with a novel bilayer structure is developed using the as-prepared PVDF film. Compared with the conventional monolayer sensor, the signal-to-noise ratio of the bilayer one is drastically improved to 38 dB from 18 dB. The above results provide great possibilities for achieving a high-performance wearable pyroelectric sensor with reduced cost and simple procedures.

Optical H2S and SO2 sensor based on chemical conversion and partition differential optical absorption spectroscopy.

An optical sensor based on chemical conversion and partition differential optical absorption spectroscopy is developed to detect hydrogen sulfide (H2S) and sulfur dioxide (SO2) gas in sulfur hexafluoride (SF6) decomposition products. Given that the absorption cross sections of SO2 and H2S overlap in 170-230 nm band, the differential lines of H2S are very few, meanwhile the corresponding absorption cross sections are small in comparison to that of SO2, thus H2S can be detected by reacting with oxygen to convert to SO2 in the presence of UV light. Through the concentration variation of SO2 before and after chemical reaction, the concentration of H2S can be obtained. Meanwhile the partition differential optical absorption spectroscopy method deduced from Beer-Lambert's law is introduced to weaken the influence of electronic noise on the measuring result, especially in low concentration. The SO2 detection limit of 12 ppb per meter can be achieved. The optical sensor can measure the concentrations of H2S and SO2, so it is suitable for the fault diagnosis of gas insulated switchgear (GIS).

Risk factors of renal dysfunction and their interaction in level-low lead exposure paint workers.

BACKGROUND: To explore the effect of low-level lead exposure on renal dysfunction in paint works, and analyze the interaction between low-level lead exposure and other influence factors of renal dysfunction. METHODS: Seven hundred forty seven workers from Sany Heavy Industry Company and Xiangjiang Kansai Paint Company who have been exposed to paint were chosen by random cluster sampling. Their blood lead level and Urine ß2-micro globulin level (renal dysfunction) were tested,risk factors of renal dysfunction in paint workers and their interactions were analyzed. RESULTS: The prevalence of renal dysfunction was 12.37%. Risk factors of renal dysfunction in paint workers mainly included longer working years (OR = 1.699, 95% CI: 1.226~ 2.355), blood lead positive (OR = 2.847, 95% CI: 1.577~ 5.139) and hypertension (OR = 2.192, 95% CI: 1.103~ 4.359). Positive interaction existed between hypertension and low-level blood lead on renal dysfunction in paint workers, the RERI (Relative excess risk of interaction), API (Attributable proportions of interaction) and S(the synergy index) were 4.758, 54.5% and 2.604 respectively. CONCLUSIONS: Low-level lead exposure and hypertension not only have independent effect on renal dysfunction in paint workers, but also had obvious positive interaction in paint workers. Interventions aimed at blood lead and blood pressure at the same time will better prenvent from renal dysfunction.

[Association between low blood lead exposure and nervous system symptoms].

OBJECTIVE: To explore the association between low blood lead exposure and nervous system symptoms among the workers exposed to oil paint. METHODS: Through cluster sampling, workers with occupational oil paint exposure in 2 factories were selected to conduct a questionnaire survey, biochemical detection and health examination. χ2 test and unconditional logistic regression analysis were performed for the determinants analysis. RESULTS: A total of 525 oil paint workers completed the survey, in whom, 55 (10.5%) were blood lead positive, the mean of blood lead concentration was (0.0884±0.0539) mg/L, 278 (52.95%) had nervous system like symptoms and 69 (13.14%) had peripheral neuropathy symptoms. Multinomial logistic regression analysis showed that working age (OR=1.827), drinking (OR=1.607), health status (OR=3.862), blood lead (OR=1.983) were risk factors for nervous system like symptoms. Working age (OR=2.282), and drinking (OR=2.704) were risk factors for peripheral neuropathy. CONCLUSION: Low blood lead exposure might be associated with nervous system like symptoms.

[Blood lead level in painters and the influential factors].

OBJECTIVE: To investigate the blood lead level (BLL) in painters and the influential factors, and to provide the theory foundation for the prevention and control of lead poisoning in painters. METHODS: A total of 535 painters from Sany Heavy Industry Company Limited and Xiangjiang Kansai Paint Corporation, who participated in the health examination during October 2012 and December 2012, were recruited and divided into a positive blood lead group and a negative blood lead group according to the level of blood lead. Questionnaires survey and physical/biochemical examinations were performed. Chi-square Test and multivariate logistic regression were used to estimate the factors that contributed to the elevated blood lead. RESULTS: The results from 525 questionnaires and physical/ biochemical examination were collected. There were 55 workers (10.4%) whose BLL were 0.04 mg/L. The maximum value of BLLs was 0.35mg/L. The multivariate logistic regression showed that smoking (OR=2.424), smoking or eating in the workplace (OR=2.139), not washing hands before smoking or eating (OR=1.624), and the cycle of changing masks longer than two weeks (OR=2.158) were positively associated with BLL. CONCLUSION: Smoking, smoking or eating in the workplace, not washing hands before smoking or eating, and the cycle of changing masks longer than two weeks are risk factors for higher BLL.

Expression profiling reveals functionally redundant multiple-copy genes related to zinc, iron and cadmium responses in Brassica rapa.

Genes underlying environmental adaptability tend to be over-retained in polyploid plant species. Zinc deficiency (ZnD) and iron deficiency (FeD), excess Zn (ZnE) and cadmium exposure (CdE) are major environmental problems for crop cultivation, but little is known about the differential expression of duplicated genes upon these stress conditions. Applying Tag-Seq technology to leaves of Brassica rapa grown under FeD, ZnD, ZnE or CdE conditions, with normal conditions as a control, we examined global gene expression changes and compared the expression patterns of multiple paralogs. We identified 812, 543, 331 and 447 differentially expressed genes under FeD, ZnD, ZnE and CdE conditions, respectively, in B. rapa leaves. Genes involved in regulatory networks centered on the transcription factors bHLH038 or bHLH100 were differentially expressed under (ZnE-induced) FeD. Further analysis revealed that genes associated with Zn, Fe and Cd responses tended to be over-retained in the B. rapa genome. Most of these multiple-copy genes showed the same direction of expression change under stress conditions. We conclude that the duplicated genes involved in trace element responses in B. rapa are functionally redundant, making the regulatory network more complex in B. rapa than in Arabidopsis thaliana.

[Investigation of health status of workers exposed to low concentration cadmium in a zinc powder factory].

OBJECTIVE: To explore the health status of workers exposed to Cd at low concentration. METHODS: One hundred eighteen workers of zinc powder finishing and 34 staffs were served as the exposure group and control group, respectively. The physical examination, blood cadmium, urinary cadmium, blood lead, urinary 32-microglobin, urine creatine, chest film, pulmonary function , pure tone teat and were detected for all subjects. RESULTS: Twelve air samples from 6 monitoring points in workshop were detected, the air Cd concentrations were 0.002-0.015 mg/m³, which were under the national limit of occupational exposure. In exposure group, the rates of exceeding standards of blood Cd and urinary Cd were 65.25% and 38.16%, respectively, the rate of exceeding standards of urinary Cd for two times was 27.12%, the rate of exceeding standard of urine Cd for two times plus the positive urinary 32-microglobin was 2.54 %. In control group, the rates of exceeding national standard of blood Cd was 26.47 %, but the values of urinary Cd were normal. In exposure group, the rate of exceeding standards of urinary Cd increased with the service length. Smoking could enhance the rates of exceeding standards of blood Cd and urinary Cd. CONCLUSION: In zinc powder finishing, the low-concentration cadmium exposure could cause the occupational cadmium poisoning, the comprehensive protection measures can reduce the occupational cadmium poisoning. It is suggested that the limits of occupational exposure to cadmium should be declined.